Functions to estimate the mean squared error of prediction (MSEP), root mean squared error of prediction (RMSEP) and \(R^2\) (A.K.A. coefficient of multiple determination) for a fitted MB-PLS models. Test-set, cross-validation and calibration-set estimates are implemented.
# S3 method for mbpls
R2(
object,
estimate,
newdata,
ncomp = 1:object$ncomp,
comps,
intercept = TRUE,
se = FALSE,
...
)# S3 method for mbpls
MSEP(
object,
estimate,
newdata,
ncomp = 1:object$ncomp,
comps,
intercept = TRUE,
se = FALSE,
...
)
# S3 method for mbpls
RMSEP(object, ...)
mvrValstats returns a list with components
three-dimensional array of SSE values. The first dimension is the different estimators, the second is the response variables and the third is the models.
matrix of SST values. The first dimension is the different estimators and the second is the response variables.
a numeric vector giving the number of objects used for each estimator.
the components specified, with 0 prepended
if intercept is TRUE.
TRUE if
comps was NULL or not specified.
The other functions return an object of class "mvrVal", with
components
three-dimensional array of estimates. The first dimension is the different estimators, the second is the response variables and the third is the models.
"MSEP",
"RMSEP" or "R2".
the components specified, with
0 prepended if intercept is TRUE.
TRUE if comps was NULL or not
specified.
the function call
an mvr object
a character vector. Which estimators to use. Should be a
subset of c("all", "train", "CV", "adjCV", "test"). "adjCV"
is only available for (R)MSEP. See below for how the estimators are chosen.
a data frame with test set data.
a vector of positive integers. The components or number of components to use. See below.
logical. Whether estimates for a model with zero components should be returned as well.
logical. Whether estimated standard errors of the estimates should be calculated. Not implemented yet.
further arguments sent to underlying functions or (for
RMSEP) to MSEP
Kristian Hovde Liland
RMSEP simply calls MSEP and takes the square root of the
estimates. It therefore accepts the same arguments as MSEP.
Several estimators can be used. "train" is the training or
calibration data estimate, also called (R)MSEC. For R2, this is the
unadjusted \(R^2\). It is overoptimistic and should not be used for
assessing models. "CV" is the cross-validation estimate, and
"adjCV" (for RMSEP and MSEP) is the bias-corrected
cross-validation estimate. They can only be calculated if the model has
been cross-validated. Finally, "test" is the test set estimate,
using newdata as test set.
Which estimators to use is decided as follows (see below for
pls:mvrValstats). If estimate is not specified, the test set
estimate is returned if newdata is specified, otherwise the CV and
adjusted CV (for RMSEP and MSEP) estimates if the model has
been cross-validated, otherwise the training data estimate. If
estimate is "all", all possible estimates are calculated.
Otherwise, the specified estimates are calculated.
Several model sizes can also be specified. If comps is missing (or
is NULL), length(ncomp) models are used, with ncomp[1]
components, ..., ncomp[length(ncomp)] components. Otherwise, a
single model with the components comps[1], ...,
comps[length(comps)] is used. If intercept is TRUE, a
model with zero components is also used (in addition to the above).
The \(R^2\) values returned by "R2" are calculated as \(1 -
SSE/SST\), where \(SST\) is the (corrected) total sum of squares of the
response, and \(SSE\) is the sum of squared errors for either the fitted
values (i.e., the residual sum of squares), test set predictions or
cross-validated predictions (i.e., the \(PRESS\)). For estimate =
"train", this is equivalent to the squared correlation between the fitted
values and the response. For estimate = "train", the estimate is
often called the prediction \(R^2\).
mvrValstats is a utility function that calculates the statistics
needed by MSEP and R2. It is not intended to be used
interactively. It accepts the same arguments as MSEP and R2.
However, the estimate argument must be specified explicitly: no
partial matching and no automatic choice is made. The function simply
calculates the types of estimates it knows, and leaves the other untouched.
Mevik, B.-H., Cederkvist, H. R. (2004) Mean Squared Error of Prediction (MSEP) Estimates for Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR). Journal of Chemometrics, 18(9), 422--429.
mbpls
data(oliveoil, package = "pls")
mod <- pls::plsr(sensory ~ chemical, ncomp = 4, data = oliveoil, validation = "LOO")
RMSEP(mod)
if (FALSE) plot(R2(mod))
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